Until now, the best results in this field have been obtained using invasive systems based on intracortical electrodes. Non-invasive systems using electroencephalographic (EEG) signals have also been tested, but they suffer from the low frequency resolution of these signals. Use of electrocorticographic (ECoG) signals, acquired by intracranial electrodes not penetrating the brain cortex, constitutes a promising intermediate solution.
Conventional BCI systems use a limited number of “features” extracted from EEG or ECoG signals to generate command signals for an external device. These features can be related e.g. to the spectral amplitudes, in a few determined frequency bands, of ECoG signals generated by specific regions of the cortex when the subject imagine performing predetermined action. As a result, only a few features of the signal are used, while the other features of the signal are not taken into account. This approach is not completely satisfactory as, for any different command signal to be generated (e.g. vertical or horizontal movement of a cursor on a screen) it is necessary to identify different features, associated to different actions imagined by the subject and substantially uncorrelated from each other. Moreover, it is intrinsically inefficient as only a small amount of the information carried by the acquired ECoG signals is exploited.
The paper by Zenas C. Chao, Yasuo Nagasaka et Naotaka Fujii “Long term asynchronous decoding of arm motion using electrocorticographic signals in monkeys”, Frontiers in Neuroengineering, Vol. 3, Art. 3, Mar. 30, 2010 describes a method of decoding (i.e. predicting) the motion of a monkey arm by applying PLS (Partial Least Squares) regression to wavelet-transformed ECoG signals. Such an approach allows a more efficient exploitation of the information carried by neuronal signals, and does not rely on predetermined “features” of said signals.
Document WO 2011/144959 discloses a BCI method wherein control signals for an external device or machine are generated by applying multi-way regression (e.g. N-way PLS, or NPLS) to neuronal signals represented as three-way tensors, said three ways corresponding to time, frequency and space. Use of multi-way instead of more conventional multiple regression (e.g. PLS) allows an even more efficient use of information.
Prior art BCI methods based on regression (either multi-linear or multi-way) suffer from some drawbacks. Notably:                “Background” (non-task related) brain activity generates noise-like parasitic signals, which in turn generate spurious low-amplitude command signals. If the BCI is used e.g. to control a robotic arm, these spurious command signal induce a tremor of the arm in the absence of voluntary motion.        Muscular contraction (in particular, mastication) generates artifacts in the form of sharp peaks with large amplitude. If the BCI is used e.g. to control a robotic arm, these artifacts can induce large, unwanted motions.        
Moreover, background brain activity and muscular artifacts are also suitable to “pollute” the data set used for learning the regression model used for command signal generation.
The paper of Kentaro Shimoda et al. “Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques”, Journal of Neural Engineering, Vol. 9, No. 3 discloses a method for detecting mastication artifact and eliminating them from the training data set of a regression model. However, this method cannot be applied “online” (in real time), during the application of the model.